Analysis of cognitive status through object interaction

ABSTRACT

Embodiments of the present invention provide a circuit board enclosed in an encasing with processors and memory, configured to receive and analyze data, and containing computer logic capable of receiving and analyzing data. The apparatus further includes sensors connected to the processors configured to transfer data to the processors, a power source configured to provide power to the processors, memory modules and sensors, one or more of a light source, an audio source, a vibration source, and a video source, a timing device, a wireless component and/or a wired component capable of transferring data, a light sensor capable of determining the intensity of the received light, computer logic capable of generating a report or transferring the data to a source capable of generating the report, where the report is a cognitive assessment, a comparison of cohorts, or a determination of how the subject puts together more an apparatus with a second apparatus, and where the cohorts are people with same or similar diseases, conditions, ages, medical histories, social demographics, experience levels, or locations.

BACKGROUND OF THE INVENTION

The present invention relates generally to cognitive testing, and moreparticularly to testing an individual's cognitive status by analyzingthe individual's handling of, and interactions with, certain objects.

Many individuals facing cognitive issues or deteriorating cognitivestatus show physical or emotional signs of the issue. Some of thesesigns may be obvious to a human observer, but some may be more subtle orrequire multiple points of analysis to fully analyze the issue. Usingvarious objects, and the ways in which individuals interact with,handles, and respond to these objects can create multiple points ofanalysis from which can be determined which disorder the individualsuffers from and the severity of the disorder.

SUMMARY

According to one aspect of the present invention, there is provided acircuit board, enclosed in an encasing, having one or more processorsand one or more memory modules, wherein the one or more processors isconfigured to receive and analyze data, and wherein the one or moreprocessors contains computer logic capable of receiving and analyzingdata; one or more sensors operatively connected to the one or moreprocessors, wherein the one or more sensors are configured to transferdata to the one or more processors; a power source disposed in thecircuit board, wherein the power source is configured to provide powerto the one or more processors, memory modules, and sensors; one or morefeatures operatively connected to the one or more processors, whereinthe one or more features are one or more of: a light source, an audiosource, a vibration source, and a video source; a timing deviceoperatively coupled to the one or more features, wherein the timingdevice switches one or more features into an on or off state, dependentupon previously determined criteria; one or more of a wireless componentor a wired component operatively coupled to the one or more processors,wherein the one or more of the wireless component or the wired componentis capable of transferring data between the one or more processors andone or more external communication sources; wherein one of the one ormore sensors comprises a light sensor configured to receive light fromthe light source feature and to determine the intensity of the lightreceived from the light source feature; and wherein the one or moreprocessors contains computer logic capable of one or more of generatinga report based on the received and analyzed data, or transferring thereceived and analyzed data to a source capable of generating the report;wherein the report is one or more of a cognitive assessment of asubject, a comparison of cohorts, or a determination of how the subjectputs the apparatus together with a second apparatus based on one or moreof a set of instructions, a target structure, an audio aid, or a visualaid; and wherein the cohorts are people with one or more of the same orsimilar: diseases, conditions, ages, medical histories, socialdemographics, experience levels, or locations.

According to another aspect of the present invention, there is provideda circuit board, enclosed in an encasing, having one or more processorsand one or more memory modules, wherein the one or more processors isconfigured to receive and analyze data, and wherein the one or moreprocessors contains computer logic capable of receiving and analyzingdata; one or more sensors operatively connected to the one or moreprocessors, wherein the one or more sensors are configured to transferdata to the one or more processors; a power source disposed in thecircuit board, wherein the power source is configured to provide powerto the one or more processors, memory modules, and sensors; one or morefeatures operatively connected to the one or more processors, whereinthe one or more features are one or more of: a light source, an audiosource, a vibration source, and a video source; a timing deviceoperatively coupled to the one or more features, wherein the timingdevice switches one or more features into an on or off state, dependentupon previously determined criteria; one or more of a wireless componentor a wired component operatively coupled to the one or more processors,wherein the one or more of the wireless component or the wired componentis capable of transferring data between the one or more processors andone or more external communication sources; wherein one of the one ormore sensors comprises a light sensor configured to receive light fromthe light source feature and to determine the intensity of the lightreceived from the light source feature; and wherein the one or moreprocessors contains computer logic capable of one or more of generatinga report based on the received and analyzed data, or transferring thereceived and analyzed data to a source capable of generating the report;wherein the report is one or more of a cognitive assessment of asubject, a comparison of cohorts, or a determination of how the subjectputs the apparatus together with a second apparatus based on one or moreof a set of instructions, a target structure, an audio aid, or a visualaid; and wherein the cohorts are people with one or more of the same orsimilar: diseases, conditions, ages, medical histories, socialdemographics, experience levels, or locations; wherein one of the one ormore sensors comprises a pressure sensor configured to sense pressurevariations above a configurable threshold; wherein the one or moreprocessors receives the sensed pressure variations; wherein the one ormore processors stores the sensed pressure variations as analyzabledata; wherein one of the one or more sensors comprises a light sensorconfigured to receive light from the light source feature and todetermine the intensity of the light received from the light sourcefeature; wherein one of the one or more sensors comprises a contactsensor; the contact sensor have an internal side connected to theencasing and an external side facing away from the encasing; theexternal side configured to sense contact with an object; wherein theone or more processors receives and records the sensed contact; whereinthe one or more processors stores the sensed contact as analyzable data;one or more timing chips operatively coupled to one or more sensors andone or more processors; wherein the one or more timing chips determineslength of time of the sensed pressure variations and the length of timeof the sensed contact; wherein the one or more timing chips transfersthe determined length of time to the one or more processors; wherein oneof the one or more sensors comprises a motion sensor configured to senseone or more of speed of motion, relative position in space, andtrajectory of motion of the encasing; wherein the one or more processorsreceives the sensed one or more of speed of motion, relative position inspace, and trajectory of motion of the encasing; wherein the one or moreprocessors stores the sensed one or more of speed of motion, relativeposition in space, and trajectory of motion of the encasing asanalyzable data; wherein the report is generated using, at least inpart, a deep neural net to determine how the subject puts the apparatustogether with the second apparatus based on the set of instructions;wherein the determination of how the subject puts the apparatus togetherwith the second apparatus is based on one or more of: border alignmentof the apparatus with the second apparatus or how tightly the apparatusis put together with the second apparatus; wherein the report isgenerated using, at least in part, a set of radio-frequencyidentification (RFID) tags to determine how the subject puts theapparatus together with the second apparatus based on the set ofinstructions; wherein the determination of how the subject puts theapparatus together with the second apparatus is based on one or more of:border alignment of the apparatus with the second apparatus or howtightly the apparatus is put together with the second apparatus; andwherein the apparatus broadcasts or displays the set of instructions byone or more of an audio device and a video device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a cloud computing environment, in accordance with anembodiment of the present invention;

FIG. 2 is abstraction model layers, in accordance with an embodiment ofthe present invention;

FIG. 3 is a functional block diagram illustrating a data processingenvironment, in accordance with an embodiment of the present invention;

FIG. 4 is flowchart illustrating operational steps for obtaining,evaluating, and analyzing an individual's cognitive data, and generatingfindings, in accordance with an embodiment of the present invention;

FIG. 5 is a visual illustration of a chart in a cognitive analysisreport, in accordance with an embodiment of the present invention;

FIG. 6A is a visual illustration of a smart modular building block, inaccordance with an embodiment of the present invention;

FIG. 6B is a visual representation of interconnected smart modularblocks, in accordance with an embodiment of the present invention;

FIG. 7 is a visual illustration of a graph used to predict variouscognitive impairments with a certain degree of confidence, in accordancewith an embodiment of the present invention;

FIG. 8 is a visual representation of a battery conserving smart block,in accordance with an embodiment of the present invention;

FIG. 9 is an example of a performed analysis, in accordance with anembodiment of the present invention; and

FIG. 10 is a block diagram of internal and external components of thecomputing device of FIG. 3, in accordance with an embodiment of thepresent invention.

DETAILED DESCRIPTION

Certain cognitive impairments come with physical and emotional signs.These signs may be subtle, or may be shown over a host of indications.Analysis of the indications may give rise to knowledge of a person'sdeterioration towards, or recovery from, such cognitive impairments.

Embodiments of the present invention recognize the need to analyze ahost of indications, using various smart objects, such as a smart block,smart pen, etc., and the ways in which an individual, also called asubject, interacts with those objects, in order to more fully determinemental cognition. For example, when a subject interacts with a set ofsmart modular building blocks (hereinafter “blocks”), such as plasticset of toy building blocks, pressure applied to the blocks, the abilityto construct the model based on a set of instructions, focus whileperforming the task, and other factors, may be indications of impairmentto cognition. Embodiments of the present invention provide solutions forevaluating and analyzing cognitive capabilities and body motor skills,and the various indications found therein, to more accurately determinea subject's deteriorating cognitive state, or recovery from cognitiveimpairment. In this manner, as discussed in greater detail herein,embodiments of the present invention can provide solutions for improvinganalysis of an individual's cognitive state by utilizing smart objectsto learn about the subject's cognitive capabilities, and, whenapplicable, predict decline or recovery of the subject's cognitivestate.

The present invention will now be described in detail with reference tothe Figures. FIG. 1 is a cloud computing environment, in accordance withan embodiment of the present invention. It is to be understood thatalthough this disclosure includes a detailed description on cloudcomputing, implementation of the teachings recited herein are notlimited to a cloud computing environment. Rather, embodiments of thepresent invention are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 50 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and security analysis 96.

FIG. 3 is a functional block diagram illustrating a data processingenvironment, generally designated 100, in accordance with an embodimentof the present invention. Modifications to data processing environment100 may be made by those skilled in the art without departing from thescope of the invention as recited by the claims. In an exemplaryembodiment, data processing environment 100 includes cloud environment120, computing device 130, and data sources 140 all interconnected overnetwork 110.

Network 110 can be, for example, a local area network (LAN), a wide areanetwork (WAN) such as the Internet, or a combination of the two, and caninclude wired, wireless, or fiber optic connections. In general, network110 can be any combination of connections and protocols that willsupport communication and/or access between cloud environment 120 andcomputing device 130.

Computing device 130 includes UI 132, system&programs 134, and datarecording devices 136. In various embodiments of the present invention,computing device 130 can be a laptop computer, a tablet computer, anetbook computer, a personal computer (PC), a desktop computer, a servercomputer, a personal digital assistant (PDA), a smart phone, a thinclient, or any programmable electronic device capable of executingcomputer readable program instructions. Computing device 130 may includeinternal and external hardware components, as depicted and described infurther detail with respect to FIG. 10.

UI 132 is a user interface that can display text, documents, web browserwindows, user options, application interfaces, and instructions foroperation. In this embodiment, UI 132 may be, for example, a graphicaluser interface (GUI) or a web user interface (WUI). UI 132 may alsoinclude the information a program presents to a user (such as graphics,text, and sound) and the control sequences the user employs to controlthe program. UI 132 is capable of receiving data, user commands, anddata input modifications from a user. UI 132 is also capable ofcommunicating with system&programs 134. In some embodiments, UI 132 cancommunicate with and control data recording devices 136.

System&programs 134 is any of a variety of software on computing device130. This software may include any system software that manages computerhardware and software resources, computer programs, libraries andrelated non-executable data, applications such as word processors,spreadsheets, antivirus software, etc., internet browsers, devicedrivers, databases, etc. System&programs 134 may interact with UI 132,cloud environment 120, network 110, data sources 140, other computingdevices and peripherals (not shown), etc. In some embodiments,system&programs 134 perform functions such as capturing, storing, andtransferring the data from data recording devices 136, as needed.

Data recording devices 136 is any of a variety of devices used to recordmovement, sound, color, pressure, etc. Additionally, data recordingdevices 136 may be only one device, one integrated system, multipledevices that are external to computing device 130, multiple standalonedevices that are directly connected to computing device 130, etc. Thedata recorded from data recording devices 136 may be transferreddirectly to, or stored and input at a later time into, system&programs134, cognition analysis 122, etc. In one embodiment, data recordingdevices 136 may be an external data recording device, such as a videocamera that is used to film a subject building a requested model out ofblocks or an audio recording device that is used to record the soundsthe subject makes while building the requested model. In anotherembodiment, data recording devices 136 may be sensors in the blocks,such as pressure sensors that record the pressure exerted on the blocksby the individual using them. This pressure may be the pressure theindividual uses to hold the block, the pressure exerted on two blocksbeing connected together, etc.

Cloud environment 120 is a cloud based computing environment, andincludes cognition analysis 122. In this embodiment, cloud environment120 is a network of servers with various functions, which areaccessible, generally, from anywhere with an internet connection. Forexample, some of the servers that make up cloud environment 120 may usecomputing power to run applications, while other servers may be used forstoring data. Cloud environment 120 may be a small or large network ofservers, and may be housed locally to computing device 130, such as inthe same building, or may be housed globally, such as in a differentcountry. In additional embodiments, the servers for cloud environment120 are housed in multiple locations at the same time, and connected toeach other over network 110. In general, cloud environment 120 cancomprise an environment having one or more components as previouslydescribed in greater detail with respect to FIG. 1 and FIG. 2.

Cognition analysis 122, in accordance with an embodiment of the presentinvention, is a program through which data is analyzed, and a report onthe cognitive status of a subject, based on the analysis, is generatedand returned. Cognition analysis 122 receives or accessed data to beanalyzed from various sources, such as data sources 140 and datarecording devices 136. In some embodiments, cognition analysis 122 usesmethods such as a cognitive computing platform to analyze the data,learn about the subject (e.g., a subject's habits, stresses,intelligence levels, good days, bad days, etc.), predict a subject'sdecline or recovery, learn and understand exceptions to the data (e.g.,a subject one day might lack focus due to a stressor in their life, suchas a loved one being hurt, and through cognitive computing learningtechniques, cognition analysis 122 determines with some degree ofcertainty that the data is an outlier and may be discarded), etc., asdescribed in greater detail in FIG. 4. Cognitive computing may be anycombination of machine learning techniques, natural language processing,human-computer interaction, other artificial intelligence or signalprocessing means, etc.

In some embodiments, cognition analysis 122 is stored on the smartblock, as described in more detail in FIG. 6A. In this embodiment,cognition analysis is stored direct on the smart block, and performs itsfunctions (i.e., FIG. 4) onboard the smart block, utilizing the smartblock's processors, memory, sensors, etc. (i.e., FIG. 6A).

Data sources 140, in accordance with an embodiment of the presentinvention, may include, but are not limited to: previously recorded dataabout the user, additional data gathered from the user (e.g., medicaldata, personal data, family data, etc.), scientific and medical data oncognitive function or diseases linked to cognitive impairment, datanecessary to perform analysis, etc. Cognitive function may includeimpairments, but also may include the normal or advance functions of asubject or subjects. For example, if a subject's cognitive function isabove their average. In this exemplary embodiment, data sources 140 arestored remotely, such as on a server (not depicted), and may be accessedvia network 110. In other embodiments, data sources 140 may be storedlocally, such as on computing device 130, on cloud environment 120, ormay be stored in a combination of storage methods.

FIG. 4 is a flowchart 200 illustrating operational steps for obtaining,evaluating, and analyzing an individual's cognitive data, and generatingfindings, in accordance with an embodiment of the present invention.

In step 202, cognition analysis 122 obtains cognitive data. In thisexemplary embodiment, cognitive data includes data recorded by datarecording devices 136, and data from data sources 140. For example, arecording device may be set up in a room where a subject is givenobjects, toys, etc., and told to perform a task requiring fine motorskills. The subject may be given a set of blocks and a set ofinstructions on a type of building to build with the blocks (e.g., adiagram, a written set of instructions, a picture of what the finishedproduct is supposed to look like, a previously built or created model ortarget structure for comparison, an audio or visual aid, etc.). Therecording of the subject's hands, face, body, or any combination thereofis then sent to cognition analysis 122. Additionally, cognition analysis122 accesses scientific and medical data on cognitive degenerativedisorders and their physical symptoms from data sources 140. In otherembodiments, the data from data recording devices is a set of data frompressure sensitive blocks, such as those described in further detail inFIGS. 6A, 6B, and 8. In yet other embodiments, cognition analysis 122already has, or has already accessed, previously recorded data on thesubject, and adds to that previous data with the new subject data. Inadditional embodiments, data points such as the subject's timing,hesitations, choice of blocks, accuracy of model with respect to theinstructions, etc. are recorded.

In step 204, cognition analysis 122 analyzes the cognitive data. In thisexemplary embodiment, cognition analysis 122 performs the analysis onrecordings obtained from data recording devices 136, and determinesvarious signs of cognitive capabilities and body motor skills. In oneexample, cognition analysis 122 determines from the recordings that thesubject is rarely concentrating on the task at hand and is easilydistracted by minor things that are happening in the room. In anotherexample, the subject forgets what they are doing and stops performingthe task at hand. Cognition analysis 122 may use recorded visual cues torecognize forgetfulness or confusion in the subject. In yet anotherexample, cognition analysis 122 may use the recordings to determine theface of the subject exhibits emotional signs, such as frustration,anger, happiness, etc., while performing the task. In still otherexamples, the pressure recordings from the blocks, as described infurther detail in FIGS. 6A, 6B, and 8 are used by cognition analysis 122to determine that the subject is unable to exert the same amount ofpressure while performing the task as the subject was able to theprevious time the body motor skill was performed. In other words,several of the same or similar tests have been performed by the subjectover a period of time (e.g., once a month for the last 6 months), andeach time the subject is able to exert less pressure putting the brickstogether than they were the time before.

In yet other examples, cognition analysis 122 performs analysis on asubject that has no cognitive impairment. In this example, cognitionanalysis measures the subject's responses to test at what level they areoperating at. For instance, an athlete may want to check to see if aspecific set of exercises that they performed that day allowed theircognitive function to increase to a level above where they normallyoperated. Cognition analysis 122 uses data obtained from recordingdevices 136, the smart blocks, the subject's past history, etc. in orderto determine the subject's operating levels (i.e., optimal, normal,etc.) and to compare the subject's current state to the variousdetermined levels. Specifically, cognition analysis 122 performsanalysis using one or more of several analysis models, such as the modeldescribed in greater detail in FIG. 9.

In step 206, cognition analysis 122 produces a cognitive analysisreport. In this exemplary embodiment, cognition analysis 122 creates acognitive analysis report based on the analysis of the recorded data onthe subject (i.e., step 204). The cognitive analysis report may includesuch items as an increase or a decrease in the subject's body motorskill performance levels, a likelihood finding of a specific disease orimpairment, snapshots of specific moments that were important to theanalysis, comparison graphs or charts of the subject versus normal orimpaired data findings, if the subject's cognitive function is operatingat normal or above normal when compared to their own data or data ofothers, etc. For example, the subject may have been tested once a monthfor the past year. Cognition analysis 122 finds through analysis of thecurrent and historic data that the subject's grip strength continues todecline. Cognition analysis 122 creates a cognitive analysis report thatcontains a graph wherein the subject's grip strength is plotted so as toshow a visual representation of the decline in grip strength over time.In some embodiments, analysis of video taken of the subject's face whilethe subject is performing the assigned task has sections taken from thevideo showing emotions that relate to the analysis. For example, asection may show that the subject is frustrated while working on a verysimple task, or in the middle of performing the task, the subject beginsto lose focus and the analysis of the section shows that the subjectbegins to pay attention to another point, and not on the task at hand,or forgets what is happening in the middle of performing the task and sothe subject's face shows bewilderment.

In other embodiments, the cognitive analysis report creates comparisonsof the findings to scientific and medical data on various diseases thatcognition analysis 122 determines are possibly relevant to the subject'srecorded data, based on a predetermined or learned threshold. Forexample, cognition analysis 122 creates a chart (e.g., FIG. 5) comparingthe decline in the subject's grip strength, and the recordings of facialemotions, to those patients diagnosed with Parkinson's or Alzheimer's orthose patients in the normal range for people without Parkinson's orAlzheimer's. In another example, a young subject's recorded data showingthe subject continuously picked up and placed the task down, and thefacial expression recordings showing the subject's eyes constantlyshifting and focusing on other details of the room are presented side byside with data from children diagnosed with ADHD. These comparisons mayinclude timing, number of tries, precision, focus, ability to followtask instructions, etc., and may contain percentages of how likely thesubject is to have a particular impairment. In yet other examples, asubject's recorded data may show that the subject has issues withfollowing instructions based on colors or shapes, and can be compared tothose with colorblindness or agnosia, or just that the subject's spatialskills are not yet developed enough. In yet other examples, the subjecthas no cognitive impairment, and is instead testing to see if thesubject is operating at average or above average performance. Forinstance, if a subject is an athlete that wants to test to see if a longnight the night before has hampered their ability to perform at anoptimal or above average level, the subject may utilize the system forthis purpose, and cognition analysis 122 generates a report based onthis information.

In some embodiments, cognition analysis 122 generates an interactivereport. In one example, the report may query the subject or the personstesting the subject as to whether the tests should be more or lesscomplex, if there should be more or less tests, or if there are updatesthat would change the outcome of the report and would not necessarily beobtained through other records, such as the subject woke up with a cold,is hung over, or has not slept in the last 24 hours. Depending on theanswers given, cognition analysis 122 may update the report as needed.In another example, cognition analysis 122 may present basic suggestionsfor how to improve the subject's performance with an option for moreinformation. If the subject chooses one of the suggestions, cognitionanalysis 122 may then present more information, such as data on why andhow the option works, how to perform the option, or links to externalinformation.

FIG. 5 is a visual illustration of a chart in a cognitive analysisreport, in accordance with an embodiment of the present invention. Inthis embodiment, cognition analysis 122 creates a cognitive analysisreport based on multiple factors, such as age 504, patient number 506,pressure on bricks 508, and emotion 510. Cognition analysis 122 utilizesthe information represented in chart 500 to distinguish patients byspecific factors, such as the age range of 61-70 listed in block 514 andthe patient number 17NM listed in block 516. These distinguishingcharacteristics can not only allow the doctors or researchers to linkthe results with the correct patient, but also can be utilized tocompare the patient with medical and scientific cognitive impairmentdata. For instance, patient 17NM (i.e., data from block 516), whileperforming a given task, is analyzed as being frustrated 60% of thetime, surprised 10% of the time, and happy 35% of the time, and the datais placed in block 518, under the emotion 510 column. Some of theseemotions may overlap, as a patient may be surprised, but happy at thesame time. This data is then compared to the data for a normal personwithout cognitive impairment, such as is found in block 520, under thenormal 512 column. In this embodiment, the analysis determines thatthese numbers for this age range is above the threshold for Parkinson'sdisease, and so the patient's data is input into the report under theheading Situation: Parkinson's disease 502 to reflect these findings.The doctors and researchers may then use this analysis to help diagnosethe patient.

In some embodiments, some findings, such as those in block 518, may becoupled with recorded proof either in the same chart or a separate chart(not shown) so that those studying the patient have records of whycognition analysis 122 determined that the patient was displaying afrustrated emotion 60% of the time.

In other embodiments, cognition analysis 122 may include in the chartthe data for only one patient, and may include current data, historicdata, or both. Cognition analysis 122 may also include a comparison ofthe current and historic data, such as determining if the patient'scognitive impairment is improving or declining over time.

In still other embodiments, the data is used for testing other cognitivefunctions beyond cognitive impairments, and the graphs reflect thesetesting purposes. For instance, a school may decide that they want touse the smart blocks to test children on their improvement of certainskill sets, or whether they are learning proper skills to advance to thenext grades. The tests may determine such things as if a kindergartenstudent has sufficiently learned colors, by instructing the child toonly use a single, specific color when building a structure, andcognition analysis 122 recognizes whether this instruction was followedand how often, and builds a graph to reflect this data. In anotherexample, the subject is an athlete that wants to perform the tests andplot a graph showing their cognitive function or motor skill function ascompared to their previous attempts, in order to determine if they areperforming at a rate above their average ability. Upon completion of thetest, cognition analysis 122 analyzes the results and creates a graphshowing the cognitive and motor skill functions of this test as comparedto previous tests the subject performed, to show whether the subject wasperforming at an above average ability.

FIG. 6A is a visual illustration of a smart block 600, in accordancewith an embodiment of the present invention. In this exemplaryembodiment, smart block 600 is a smart modular building block containsmultiple features and sensors, such as connector 605, motion sensor 610,contact sensor 615, processing unit 620, and wireless component 625.

Processing unit 620 is a computer processing unit with memory, utilizingcomputer logic to perform multiple functions. Processing unit 620'smultiple functions may be storing the block's color, id, and currentconnections, sending and receiving information from the various featuresand sensors, storing and processing data, etc. In some embodiments,processing unit 620 may process and/or analyze the data from thesensors, coordinate the transmission of data of the current assembly toremote equipment or to cognition analysis 122, etc.

In other embodiments, processing unit 620 contains cognition analysis122. In this embodiment, processing unit 620 receives information fromsmart block 600's respective features and sensors, and utilizes theonboard cognition analysis 122 to evaluate and analyze the subject'scognitive data and findings, and generate reports (i.e., FIG. 4). Smartblock 600 may perform this analysis without utilizing outside computing,such as utilizing computing device 130, or may access and utilize otherresources, such as a cognitive computing platform, directly or overnetwork 110. In some embodiments, multiple smart block 600 s may worktogether to obtain, evaluate, and analyze the subject's cognitive dataand findings, and generate reports. For example, when two or more smartblock 600 s are connected to each other, the processing unit 620 fromone block may interact with processing unit 620 from the other smartblock. In this example, the two processing unit 620 s may perform ananalysis of the data together or one of the processing unit 620 mayaccess the features and sensors of the other smart block, and performany functions that the other smart block may perform.

Connector 605 is a sensor that registers contact with other devices andother sensors to indicate if the connection point is connected toseparate object, such as is shown in more detail in FIG. 6C, and thencommunicates this information to processing unit 620. In someembodiments, the connectors, such as connector 605, may communicate witheach other directly as well as communicating with processing unit 620.

Motion sensor 610 may sense and register such items as the block's speedof motion, relative position in space, etc. For example, motion sensor610 may register not only that the block was moving through space in aspecific trajectory, but that while moving in that forward trajectory,the block was also moving in a slight, continuous back and forth motionat the same time. This data, when analyzed, could show that the patientis showing signs of a slight tremor when moving. This data could behelpful in diagnosing the specific cognitive disability the patient has.

Contact sensor 615 senses and registers pressure, and communicates thisinformation to processing unit 620. For example, if a subject picks upthe block, and exerts 0.3 pounds per square inch on the block in orderto keep the block held between the subject's fingers, contact sensor 615will transmit this information to processing unit 620 to be stored andutilized by cognition analysis 122 in its assessment of the patient'scognitive state.

Wireless component 625 is a component that allows for data transfer toan external communication source, such as to cognition analysis 122.Wireless component 625 may be any of a multitude of communication types,such as Bluetooth, radio frequency identification (RFID), near fieldcommunication (NFC), etc. In some embodiments, wireless component 625may be a wired communication type, such as an Ethernet port, or mayinclude both wireless and wired communication. In other embodiments,wireless component 625 may broadcast instructions, audio commands, orvarious sounds through such means as a headset or speaker system.

In some embodiments, each feature and sensor is assigned a uniqueidentifier. For instance, connector 605 at the top left of the block maybe assigned the unique identifier “1” and the next connector 605, ifgoing in a clockwise pattern from “1,” may be assigned the uniqueidentifier “2,” then “3,” etc. These unique identifiers allow processingunit 620 and cognition analysis 122 to separate or combine data fromeach sensor in order to analyze the data more accurately.

In other embodiments there may be more or less of each of the featuresand sensors, and there may be other sensors that measure other data.Blocks may have sensors based on the function desired for those blocks,and may communicate with other blocks. For example, some blocks may onlyhave connector sensors, such as connector 605 type sensors, andprocessing unit 620. When connected to a second block, that containswireless component 625, the first block may transmit data to the secondblock, and the second block may utilize wireless component 625 totransfer not only its own data, but also the data from the first blockthat only contains multiple connector 605 sensors and processing unit620.

In yet other embodiments, the blocks may contain a means of timing theinteractions of the subject with the block. For example, there may be atiming chip that registers the data and time when the block is moved,touched, or otherwise interacted with, and the data and time when theblock is no long being moved, touched, or otherwise interacted with. Instill other embodiments, the smart modular building block may be adifferent item type, such as a pen, a ball, a figure, etc., but stillcontain the some or all of the multiple features and sensors.

In multiple exemplary embodiment, the various features and sensors areembedded or attached to the smart blocks through various means ofproduction. For example, processing unit 620 may be embedded on theinside of the smart block and connected via any combination of dataand/or electrical transfer components to other features and sensors inother parts of the smart block. Processing unit 620 may then control thedata capture, flow, and storage from one feature or sensor to another.In this example, there are one or more connector 605 and contact sensor615 operatively connected to the smart block on the top, bottom, andsides, either on the surface of the smart block, or embedded in thesmart block. The attachment point is one that allows communication withsuch items as other smart block connector 605 s and contact sensor 615s, proximity sensing of the subject, etc. Each of the one or moreconnector 605 and contact sensor 615 is then operatively connected toprocessing unit 620 allowing for data transfer, storage, and analysis tooccur, and processing unit 620 to control the one or more connector 605and contact sensor 615. For instance, processing unit 620 may turn offthe one or more connector 605 and contact sensor 615 when processingunit 620 determines that battery saving mode is required, as discussedin more detail in FIG. 8.

FIG. 6B is a visual representation of interconnected smart modularblocks, in accordance with an embodiment of the present invention. Inthis exemplary embodiment, block 640, 660, and 680 have been connectedto each other. In this example, each block has its own respectivesensors. In other words, block 640 has its own set of connector 605 sand processing unit 620, block 660 has its own set of connector 605 s,processing unit 620, and wireless component 625, etc. Various featuresand sensors are able to not only sense and register information on theirown smart block, but may also communicate with features and sensors fromother blocks. For example, respective connector 605 of block 640, 660,and 680, once they come into proximity with each other, can register howconnected they are. In this example, connector 605 s where block 660 andblock 680 meet sense and register that they are tightly connected.However, connector 605 s where block 640 and block 660 meet sense andregister that they are connected, but not tightly, as there is a smallgap between block 640 and block 660. In another example, respectiveconnector 605 of block 640, 660, and 680 register the border alignmentof block 640 and block 660 (e.g., whether the edges of the blocks lineup to make the surface plane smooth, or if the edges are slightlytwisted, tilted, etc., and so the edges of the blocks stick out fromeach other). In this exemplary embodiment, all of the sensor data fromblock 640, 660, and 680 may then be transferred to processing unit 620in block 660. The reason for this is that block 660 is the only blockthat has wireless component 625, so that all the data may be transferredto the necessary locations for the analysis by cognition analysis 122.The data transferred can be any type of data, such as block 640 and680's id, historic data, and color, as well as the data from the variousconnector 605 sensors.

In some embodiments, various features and sensors of the blocks areconfigured to respond to certain interactions in a predeterminedfashion. For example, if a subject is building a model based on receivedinstructions, and the subject decides that they are finished buildingthe model, the subject may then use a “completion” signal, such as adouble tap on the top smart block, to signal that they are finishedbuilding the model. In this example, the various features and sensors inthe smart blocks are configured to recognize the double tap as a“completion” signal and then proceeds to send all the acquired data tocognition analysis 122.

In some exemplary embodiments, the features and sensors will also senseand register the pressure, etc. of the blocks being removed from eachother, and store and transmit this data as well. For example, a doctoror scientist may want to pre-build a specific model using the smartmodular building blocks, and request that the subject separate thepieces, or may request that the subject put the model together first andthen separate the pieces. The respective connector 605 s not only senseand register when they are in proximity to, and connected with, anotherblock, but also when they are no longer in proximity to or connectedwith another block. This data could be utilized as well by cognitionanalysis 122 in order to help analyze the patient and create adiagnosis. For example, cognition analysis 122 may determine that thesubject used their palm or the weight of their body to put the blockstogether, but the subject's grip strength is deteriorating because theanalysis of the data from the respective connector 605 s shows that thesubject has difficulty pulling the blocks apart.

In other examples, the smart blocks may contain and/or use the sensors,radio-frequency identification (RFID) tags, near-field communication(NFC) tags, barcodes, deep neural neural nets, etc. to facilitateidentification of the build of the specific model. In other words, thesmart blocks work together to process how the specific model is beingbuilt and whether the model the subject is building matches theinstructions and model plans that the subject was given. In thisexample, if the built model is not correct, the smart blocks would beable to determine where the subject deviated from the instructions. Forinstance, if the instructions for a model were given that was a houseshape, with green blocks for the front of the house, yellow blocks forthe window, red blocks for the rest of the house, and was supposed to bea square shape, but the subject occasionally mixed up red and greenblocks, and added on a slight L shape to the back of the house, thesmart blocks could determine where the differences were, when during theprocess of building the model that the subject deviated from theinstructions, etc.

FIG. 7 is a visual illustration of a graph used to predict variouscognitive impairments with a certain degree of confidence, in accordancewith an embodiment of the present invention. In this exemplaryembodiment, cognition analysis 122 creates a graph utilizing dataavailable to diagnose cognitive impairment, such as medical data,scientific data, etc. In this graph, cognition analysis 122 creates thegraph with two separate dimensions to plot the data on, such asdimension 702 and dimension 704. These two dimensions may be any numberof dimensions, depending on the data being collected and the cognitiveimpairment being looked at. For example, the two dimensions may be basedoff of two dimensions of construction characterization for the assignedconstruction task (e.g., size, complexity, number of turns anticipated,anticipated time to solve, distraction level in the room, degree ofpractice, varied use of colored blocks, etc.).

Areas within the graph, such as area 706, are based on various facets ofthe medical and scientific determinants for those particular cognitiveimpairments. In this embodiment, the two dimensions cause area 706 to belabeled as the Autism Spectrum, where any points falling within thatarea, such as points 708, may be seen as being on the autism spectrum,and points falling outside that area, such as points 710, may be seen asbeing outside the autism spectrum in a more standard, or normal,response.

When cognition analysis 122 analyzes the data collected on a subject,the subject's actions are plotted as points in this graph, such aspoints 708 and points 710. The points may be anywhere in the graph,depending on the data plotted, but by plotting these points, cognitionanalysis 122 may then determine a subject's diagnosis with a certainlevel of confidence. For example, if virtually all of the data pointscollected and analyzed fell within area 706, such as in this example,cognition analysis 122 may then diagnose the subject as having autism,with a fairly high degree of certainty. On the other hand, if virtuallyall of the data points fell outside of area 706 (not shown), cognitionanalysis 122 may then diagnose the subject as not having autism, with afairly high degree of certainty.

In some embodiments, the points may be have more or less points, more orless dimensions, and more or less areas labeled for other cognitivefunctions, issues, or impairments, such as Alzheimer's, pre-Alzheimer's,Parkinson's, child development issues, a subject's above averagescoring, etc.

In other embodiments, the graphs and various analysis methods andresults are created and produced for the researcher or doctor doing thestudy of the subject, parents or caregivers of the subject, teachers orschool administrators, psychologists, therapists, artificialintelligence (AI), etc. In yet other embodiments, the graphs are usedinternally by cognition analysis 122 and are stored internally for usein later studies.

In still other embodiments, the graphs and various analysis may comewith recommendations as to treatments or further tests necessary for thesubject.

In yet still other embodiments, cognition analysis 122 analyzes data tolearn what tests are best for different cohorts, or classes of subjects.For example, cognition analysis 122 may determine that when a specificshape is built, cognition analysis 122 can determine the Alzheimer'scognitive deterioration of a subject with a higher degree of accuracythan with a different shape. Cognition analysis 122 may receive apossible diagnoses for a subject, and may request certain tests based onthis information and what cognitive analysis 122 has determined is thebest shape for the cognitive deterioration. Additionally, the cohortsmay be subjects that have previously performed tasks that were analyzedby cognition analysis 122, may be cohorts that are determined by medicalor scientific data (e.g., subjects with the same or similar diseases orconditions, age, medical history, demographic, experience level, etc.),etc.

FIG. 8 is a visual representation of a battery conserving smart block,in accordance with an embodiment of the present invention. In thisexemplary embodiment, the battery smart block is designed to be batteryconserving, so that the sensors will not continuously draw power, basedon previously determined criteria. Each battery smart block, such asblock 816 and block 818, is equipped with sensors, such as sensors 812,which act as a switch for an internal circuit breaker, such as breaker810. For example, one previously determine criteria may be that whenblock 816 and block 818 are not connected, such as in case 802, block816 and block 818 are in an off state (i.e., they are not recording dataor using battery power). In this state, light 806 (e.g., a variablecolor light-emitting diode, or LED, light) is not on. However, whenblock 816 and block 818 are connected, such as in case 804, sensors 812register this connection and light 806 turns on.

In this embodiment, the battery conserving smart block may be equippedwith a variety of battery sources, such as a lithium ion battery. Thebattery may be rechargeable, or may be powered by a battery that must bereplaced, such as a button cell battery. The battery powers the smartblock via the battery cathode and anode being connected to an electricalcircuit to feed power to the various features and sensors attached tothe smart block.

In some embodiments, the time that light 806 turns on for is specifiedby a delay timer, which may be included in breaker 810. This delay timerthat turns on light 806 for a set time and then turns light 806 offhelps to conserve battery power for block 816 and block 818.

In other embodiments, sensors 812 are sensor arrays that senses anddetermines the intensity of the light emitted from light 806. In thisembodiment, light 806 strobes or pulses light. As block 816 and block818 are being connected, sensors 812 capture the strobed or pulsed lightand determines the intensity of the light. The intensity allows for thedetermination of displacement of block 816 and block 818 along variousaxis, such as the X, Y, and Z axis, as well as pitch, yaw, and roll ofthe blocks. For example, if the subject is able to put block 816 andblock 818 together, but only the back, right corner is tightly fitted,and the front left corner is partially fitted, the light intensitycaptured at the various points by sensors 812 will determine this fit isonly a partially tight fit. The intensity of the light captured at thetightest point would be greater than at the point where the light hassome ability to escape due to the connection not being as tightlyfitted, thus allowing for spaces and gaps. Additionally, as block 816and block 818 are fitted together, sensors 812 can determine their fitproximity, the timing of their placement, and the way in which they wereplaced together (i.e., the axis information as to if the blocks were fittogether flat along the x and y axis, any amount of pitch and yaw of theblocks, etc.). This data could help determine if certain fingers of thesubject are weaker than others, or other such factors that may behelpful in determining the cognitive state of a subject. Furthermore, ifeach light sensor is uniquely identified, sensors 812 could helpdetermine the timing of when various blocks were fit together, the speedat which the task was accomplished, etc.

In still other embodiments, the smart block may be equipped withfeatures such as audio sources (e.g., speakers), vibration sources(e.g., a device that, when doubled tapped by a subject, would vibrateslightly to let the subject know that the double tap has beenregistered), or video sources (e.g., a small video screen, visualprojection capabilities, etc.). As with light 806, the features may beplaced on timers or controlled by the smart brick in order to conservebattery, or sent instructions from processor 620 (i.e., FIG. 6A). Forinstance, the audio sources may only play instructions that arepredetermined to be essential when on battery power, but if the smartblock is receiving a continuous source of power, the audio sources mayplay all of the instructions, music, subject encouragement, etc.

In additional embodiments, the smart block may conserve battery power bynot recording subject data continuously. For example, some smart blocksmay only record pressure every 10 milliseconds. Some blocks may onlyrecord when new pressure levels outside of a certain threshold isapplied. In this example, a human subject would probably not be able toapply the same and constant pressure on a block, whether the subject hascognitive deterioration or not. There will be some slight changes foranyone handling the blocks. The block takes into consideration thisthreshold and adjusts to only record when the pressure level is outsideof this pressure threshold.

In other embodiments, the smart blocks may turn on when placed to thesides touch with the sides of another block. For example, block 680 andblock 660 of FIG. 6B would, if they are conservative batter power smartblocks, be turned on by touching their sides.

In yet other embodiments, smart blocks may utilize a continuous powerflow method, such as solid state or static power supply module, or apower cord capable of transferring electrical current from a source suchas an electrical outlet. In this embodiment, the smart block is coupledwith a solid state power supply in order to achieve power conversion topower the various features and sensors of the smart block. In someexamples, this will allow the smart block to remain in an “on” state, orto continuously obtain and analyze data, unless and until the smartblock is switched into an “off” state. The “off” state may be due to asubject, doctor, or researcher switching the smart block into the “off”state, a power supply outage, etc.

In yet still other embodiments, the smart block may utilize both acontinuous power flow method and a battery power conservation method. Inthis embodiment, a smart block battery system may be recharged by thecontinuous power flow method. Additionally, the smart block may initiatean immediate switch to the battery system upon an interruption of powerfrom the continuous power source, so as not to interrupt the smartblock's performance, gathering of data, etc. In some embodiments, thesubject, doctor, scientist, teacher, etc. may control whether the smartblock immediately switches to battery power upon interruption of powerfrom a continuous power source. In other embodiments, the smart blockmay be set to initiate a system shutdown upon interruption of power froma continuous power source, in order to conserve battery power.

FIG. 9 is an example of a performed analysis, in accordance with anembodiment of the present invention. In this embodiment, the analysis isperformed using one or more of several models (m), situations (s), andevaluation and analysis methods, in accordance with an embodiment of thepresent invention. For example, the model, m, may be a TargetConstruction Model or a User Construction Model, the situation, s, maybe a child's development, someone with possible Parkinson's, or someonewith possible Alzheimer's. In this embodiment, the evaluation andanalysis may be a comparison between the target construction and theuser construction, or a learning method, such as a subject's personallearning.

In these embodiments, the Target Construction Model (tc) is conformed bya model m, actions over time a, and expected environmental conditions ec(e.g., noise, light, direct interruptions, etc., wherein the ranges foreach describe the specific situations in the locations where theconstruction is being assembled). Model m data can be detailed orsimple. In the following analysis example, A, B, and C represent smartbricks, and the numbers following the letters represent areas that thebricks are connected:

-   m=A<−>B<−>C-   m=A(Type abc, color light blue)<−>B(Type mno, color blue)<−>C(Type    xyz, color green)-   m=A(Type abc, color light blue, connections:{A4-B2, A5-B1})<−>B(Type    mno, color blue, connections:{B1-A5, B2-A4, B3-C6})<−>C(Type xyz,    color green, connections:{C6-B3})

The actions over time a in this example involves expected time range t,set of expected emotions e, and set of handling characteristics h andthe analysis would look like the following:

-   a=└t1, {e1, e2, . . . , en}, {h1, h2, . . . , hn}┘, └t2, {e1, e2, .    . . , en}, {h1, h2, . . . , hn}┘-   e={hesitant: 10-20%; confused: 5-10%; . . . ; distracted: 15-20%}-   h={squeeze: 0.2-0.4 pounds per square inch; pressure: 0.1-0.3;    lastBrickAdded:BlueRectFlat1010}-   ec={noise: 10 dBA-30 dBA; light: 501x-701x}

In these embodiments, the User Construction Model (uc) is conformed bythe final model urn, user actions over time ua, and user environmentalconditions uec (e.g., conditions that contains values captured over theuser session time for specific environmental characteristics, such asnoise and light). The uc contains data gathered during the subject'ssessions. The uc contains similar data to tc, but is specifically aboutthe subject's construction in a specific session. For instance, useractions over time (ua) involves specific time capture of user actions(ut), a set of user determined emotion (ue), and a set of sensed userhandling characteristics (uh), and the analysis would look like thefollowing:

-   ua=[ut1, {ue1, ue2, . . . , uen}, {uh1, uh2, . . . , uhn}], [ut2,    {ue1, ue2, . . . , uen}, {uh1, uh2, . . . , uhn}]-   ue={hesitant: 10%; confused: 10%; . . . ; distracted: 15%}-   uh={squeeze: 0.2, pressure: 0.1, brickInHand: RedSquare0001,    lastBrickAdded: BlueRectFlat1010}-   uec={[t1, {noise: 10 dBA, light: 501x, directDistractions: 2}], [t2,    {noise: 10 dBA, light: 701x}, directDistractions: 1]}

In these embodiments, the situation (s) to evaluate has a set ofrequired skills rs. So s has (rs1, rs2, . . . , rsn). For example, achild's development s may have to do with the following three skills:fine motor skills, follow instructions, and color recognition. Eachrequired skill rs contains a set of rated criteria c, such as {c1, c2, .. . , cn}. For example, fine motor skills may have the followingcriteria: block connection at 40%, emotion detected at 10%, order ofassembly at 10%, and handling at 40%. The rate is used to identify whichrequired skill has more weight in the s evaluation. Additionally, eachcriteria c is associated with one or more data sources d, such as {d1,d2, . . . , dn}. For example, block connection is associated with blockconnectors, emotion detected is associated with such things as gestures,face recognition, time, etc., order of assembly is associated with time,connector measurements, etc., and handling is associated with pressuresensors.

In this embodiment, each situation s is evaluated using a comparisonbetween the target construction tc and the user construction uc. Whereeach situation criteria is used to extract the corresponding data fromthe tc and uc for comparison, and criteria variations cv are calculatedfor each criteria, according to the data type. For example, row 904shows the percentage that the subject expresses a certain emotion. Therecording of the subject's face is analyzed by cognition analysis 122.In this embodiment, the analysis in column 912 of row 904 shows thetarget level for the frustrated component for tc data is 5%. However,the uc data in column 914 is actually 40%. This means that the subjectwas frustrated 40% of the time in this particular construction period.As shown in column 916, this difference creates a 35% negative change(i.e., 5%-40%=−35%). The target level for the surprised component of tcdata in column 912 is 50%. The uc data shown in column 914 is 40%. Thisdifference, shown in column 916, is only 10%. In this example, thethreshold is set such that a deviation of 10% is considered neutral.

In this embodiment, row 908 shows the target construction tc (i.e., theway the blocks should be put together) in column 920, the userconstruction uc (i.e., the way the subject puts the blocks together) incolumn 922, and the percentage that the uc matched the tc in column 924.In this example, the subject put two of the six blocks together as perthe instructions, and so the subject was correct 33.3% of the time.

This data can be used by cognition analysis 122 to help determine thecognitive function or impairment of the subject. The data can becompared to a subject's historical data (i.e., previous tests, medicalrecords, etc.), medical data and literature, scientific data andliterature, etc. In other embodiments, there may be more or less rows,columns, and data, and the thresholds and ratings (e.g., negative orneutral) may be different or have more variables.

FIG. 10 is a block diagram of internal and external components of acomputer system 400, which is representative of the computer systems ofFIG. 3, in accordance with an embodiment of the present invention. Itshould be appreciated that FIG. 10 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Ingeneral, the components illustrated in FIG. 10 are representative of anyelectronic device capable of executing machine-readable programinstructions. Examples of computer systems, environments, and/orconfigurations that may be represented by the components illustrated inFIG. 10 include, but are not limited to: personal computer systems,server computer systems, thin clients, thick clients, laptop computersystems, tablet computer systems, cellular telephones (e.g., smartphones), multiprocessor systems, microprocessor-based systems, networkPCs, minicomputer systems, mainframe computer systems, and distributedcloud computing environments that include any of the above systems ordevices.

Computer system 400 includes communications fabric 402, which providesfor communications between one or more processors 404, memory 406,communications unit 410, and one or more input/output (I/O) interfaces412. Communications fabric 402 can be implemented with any architecturedesigned for passing data and/or control information between processors(such as microprocessors, communications and network processors, etc.),system memory, peripheral devices, and any other hardware componentswithin a system. For example, communications fabric 402 can beimplemented with one or more buses.

Memory 406 and persistent storage 408 are computer-readable storagemedia. In general, memory 406 can include any suitable volatile ornon-volatile computer-readable storage media. Software (e.g.,system&programs 134, etc.) is stored in persistent storage 408 forexecution and/or access by one or more of the respective processors 404via one or more memories of memory 406.

Persistent storage 408 may include, for example, a plurality of magnetichard disk drives. Alternatively, or in addition to magnetic hard diskdrives, persistent storage 408 can include one or more solid state harddrives, semiconductor storage devices, read-only memories (ROM),erasable programmable read-only memories (EPROM), flash memories, or anyother computer-readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 408 can also be removable. Forexample, a removable hard drive can be used for persistent storage 408.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer-readable storage medium that is also part of persistent storage408.

Communications unit 410 provides for communications with other computersystems or devices. In this exemplary embodiment, communications unit410 includes network adapters or interfaces such as a TCP/IP adaptercards, wireless local area network (WLAN) interface cards, or 3G or 4Gwireless interface cards or other wired or wireless communication links.The network can comprise, for example, copper wires, optical fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers. Software and data used to practice embodiments ofthe present invention can be downloaded through communications unit 410(e.g., via the Internet, a local area network or other wide areanetwork). From communications unit 410, the software and data can beloaded onto persistent storage 408.

One or more I/O interfaces 412 allow for input and output of data withother devices that may be connected to computer system 400. For example,I/O interface 412 can provide a connection to one or more externaldevices 418 such as a keyboard, computer mouse, touch screen, virtualkeyboard, touch pad, pointing device, or other human interface devices.External devices 418 can also include portable computer-readable storagemedia such as, for example, thumb drives, portable optical or magneticdisks, and memory cards. I/O interface 412 also connects to display 420.

Display 420 provides a mechanism to display data to a user and can be,for example, a computer monitor. Display 420 can also be an incorporateddisplay and may function as a touch screen, such as a built-in displayof a tablet computer.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to: an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. An apparatus, comprising: a circuit board,enclosed in an encasing, having one or more processors and one or morememory modules, wherein the one or more processors is configured toreceive and analyze data, and wherein the one or more processorscontains computer logic capable of receiving and analyzing data; one ormore sensors operatively connected to the one or more processors,wherein the one or more sensors are configured to transfer data to theone or more processors; a power source disposed in the circuit board,wherein the power source is configured to provide power to the one ormore processors, memory modules, and sensors; one or more featuresoperatively connected to the one or more processors, wherein the one ormore features are one or more of: a light source, an audio source, avibration source, and a video source; a timing device operativelycoupled to the one or more features, wherein the timing device switchesone or more features into an on or an off state, dependent uponpreviously determined criteria; one or more of a wireless component or awired component operatively coupled to the one or more processors,wherein the one or more of the wireless component or the wired componentis capable of transferring data between the one or more processors andone or more external communication sources; wherein one of the one ormore sensors comprises a light sensor configured to receive light fromthe light source feature and to determine the intensity of the lightreceived from the light source feature; and wherein the one or moreprocessors contains computer logic capable of one or more of generatinga report based on the received and analyzed data, or transferring thereceived and analyzed data to a source capable of generating the report;wherein the report is one or more of a cognitive assessment of asubject, a comparison of cohorts, or a determination of how the subjectputs the apparatus together with a second apparatus based on one or moreof a set of instructions, a target structure, an audio aid, or a visualaid; and wherein the cohorts are people with one or more of the same orsimilar: diseases, conditions, ages, medical histories, socialdemographics, experience levels, or locations.
 2. The apparatus of claim1, wherein the one or more sensors comprises a pressure sensorconfigured to sense pressure variations above a configurable threshold;wherein the one or more processors receives the sensed pressurevariations; and wherein the one or more processors stores the sensedpressure variations as analyzable data.
 3. The apparatus of claim 1,wherein the one or more sensors comprises a contact sensor; the contactsensor having an internal side connected to the encasing and an externalside facing away from the encasing; the external side configured tosense contact with an object; wherein the one or more processorsreceives and records the sensed contact; and wherein the one or moreprocessors stores the sensed contact as analyzable data.
 4. Theapparatus of claim 2, further comprising: one or more timing chipsoperatively coupled to one or more sensors and one or more processors;wherein the one or more timing chips determines length of time of thesensed pressure variations; and wherein the one or more timing chipstransfers the determined length of time to the one or more processors.5. The apparatus of claim 3, further comprising: one or more timingchips operatively coupled to one or more sensors and one or moreprocessors; wherein the one or more timing chips determines length oftime of the sensed contact; and wherein the one or more timing chipstransfers the determined length of time to the one or more processors.6. The apparatus of claim 1, wherein the one or more sensors comprises amotion sensor configured to sense one or more of: speed of motion,relative position in space, or trajectory of motion of the encasing;wherein the one or more processors receives the sensed one or more of:speed of motion, relative position in space, or trajectory of motion ofthe encasing; and wherein the one or more processors stores the sensedone or more of: speed of motion, relative position in space, ortrajectory of motion of the encasing as analyzable data.
 7. Theapparatus of claim 1, wherein the report is generated using, at least inpart, a deep neural net to determine how the subject puts the apparatustogether with the second apparatus based on the set of instructions. 8.The apparatus of claim 7, wherein the determination of how the subjectputs the apparatus together with the second apparatus is based on one ormore of: border alignment of the apparatus with the second apparatus orhow tightly the apparatus is put together with the second apparatus. 9.The apparatus of claim 1, wherein the report is generated using, atleast in part, a set of radio-frequency identification (RFID) tags todetermine how the subject puts the apparatus together with the secondapparatus based on the set of instructions.
 10. The apparatus of claim9, wherein the determination of how the subject puts the apparatustogether with the second apparatus is based on one or more of: borderalignment of the apparatus with the second apparatus or how tightly theapparatus is put together with the second apparatus.
 11. The apparatusof claim 1, wherein the apparatus broadcasts or displays the set ofinstructions by one or more of an audio device and a video device. 12.An apparatus, comprising: a circuit board, enclosed in an encasing,having one or more processors and one or more memory modules, whereinthe one or more processors is configured to receive and analyze data,and wherein the one or more processors contains computer logic capableof receiving and analyzing data; one or more sensors operativelyconnected to the one or more processors, wherein the one or more sensorsare configured to transfer data to the one or more processors; a powersource disposed in the circuit board, wherein the power source isconfigured to provide power to the one or more processors, memorymodules, and sensors; one or more features operatively connected to theone or more processors, wherein the one or more features are one or moreof: a light source, an audio source, a vibration source, and a videosource; a timing device operatively coupled to the one or more features,wherein the timing device switches one or more features into an on oroff state, dependent upon previously determined criteria; one or more ofa wireless component or a wired component operatively coupled to the oneor more processors, wherein the one or more of the wireless component orthe wired component is capable of transferring data between the one ormore processors and one or more external communication sources; whereinthe one or more processors contains computer logic capable of generatinga report based on the received and analyzed data; wherein the report isone or more of a cognitive assessment, a comparison of cohorts, or adetermination of how a subject puts the apparatus together with a secondapparatus based on a set of instructions; and wherein the cohorts arepeople with one or more of the same or similar: diseases, conditions,ages, medical histories, social demographics, experience levels, orlocations; wherein one of the one or more sensors comprises a pressuresensor configured to sense pressure variations above a configurablethreshold; wherein the one or more processors receives the sensedpressure variations; wherein the one or more processors stores thesensed pressure variations as analyzable data; wherein one of the one ormore sensors comprises a light sensor configured to receive light fromthe light source feature and to determine the intensity of the lightreceived from the light source feature; wherein one of the one or moresensors comprises a contact sensor; the contact sensor having aninternal side connected to the encasing and an external side facing awayfrom the encasing; the external side configured to sense contact with anobject; wherein the one or more processors receives and records thesensed contact; wherein the one or more processors stores the sensedcontact as analyzable data; one or more timing chips operatively coupledto one or more sensors and one or more processors; wherein the one ormore timing chips determines length of time of the sensed pressurevariations and the length of time of the sensed contact; wherein the oneor more timing chips transfers the determined length of time to the oneor more processors; wherein one of the one or more sensors comprises amotion sensor configured to sense one or more of: speed of motion,relative position in space, or trajectory of motion of the encasing;wherein the one or more processors receives the sensed one or more of:speed of motion, relative position in space, or trajectory of motion ofthe encasing; wherein the one or more processors stores the sensed oneor more of: speed of motion, relative position in space, or trajectoryof motion of the encasing as analyzable data; wherein the report isgenerated using, at least in part, a deep neural net to determine howthe subject puts the apparatus together with the second apparatus basedon the set of instructions; wherein the determination of how the subjectputs the apparatus together with the second apparatus is based on one ormore of: border alignment of the apparatus with the second apparatus orhow tightly the apparatus is put together with the second apparatus;wherein the report is generated using, at least in part, a set ofradio-frequency identification (RFID) tags to determine how the subjectputs the apparatus together with the second apparatus based on the setof instructions; wherein the determination of how the subject puts theapparatus together with the second apparatus is based on one or more of:border alignment of the apparatus with the second apparatus or howtightly the apparatus is put together with the second apparatus; andwherein the apparatus broadcasts or displays the set of instructions byone or more of an audio device and a video device.